Replication Guide for Roadmaps to Representation:  An Experimental Study of How Voter Education Tools Affect Citizen Decision Making

One main dataset, roadmaps_1.dta, was used for nearly all of the analyses in Roadmaps.  The file, roadmaps_1.dta, contains the responses of voters to the online survey we conducted during the 2012 supervisorial elections in San Francisco.  The text of the policy questions, treatments and knowledge questions are available in the Online Appendix.

The following instructions will allow interested users to reproduce all analytical tables and figures in the article and online appendix.  We have included a zipped file, Roadmaps_combined.zip, that includes all files in the dataverse.  In running the command files, the user will need to correct the file paths, locating each file as appropriate.  All files referred to in this document and uploaded to the dataverse are listed and described herein.

All of the analyses in the article and supporting information were performed using STATA 13.1 and R version 3.2.5.  All of the analysis datasets are saved as text files, Excel (Microsoft Excel 2010) files, R files or STATA files.


Tables and Figures in the Article and Supporting Information

Roadmaps Table 1

The STATA do file, Table 1.do, opens the file containing voters� responses to the exit poll from 2012 (roadmaps_1.dta) and tabulates voters� responses to 16 policy questions.  The file, roadmaps_1_codebook.doc, describes the variables contained in the online exit poll file.


Roadmaps Table 2

The STATA do file, Table 2.do, opens the file containing voters� responses to the exit poll from 2012 (roadmaps_1.dta) and tabulates voters� preferences between David Lee and Eric Mar in the control and each treatment group.  The bottom of the file conducts difference of means tests of support for Lee between the control and each treatment group, and between the voter guide treatment and other treatment groups.  The results of these analyses are summarized in Table 2.


Roadmaps Figure 3 and Tables A3 and A4 in the OA

The STATA do file, Tables A3 and A4.do, opens the analysis dataset, roadmaps_1.dta, recodes variables for treatment and control group assignment, and estimates a probit model of voters� preferences between David Lee and Eric Mar.  The bottom of the file uses the CLARIFY package to generate predicted probabilities and first differences, and compare predicted probabilities and first differences across the control and treatment groups.  These predicted probabilities and first differences are saved in two text files (Table A3.log and Table A3 any info.log) and one Excel file (Table A3 simqi.xlsx).  The predicted probabilities, first differences and tests of the differences between control and treatment groups are summarized in Table A4 in the OA.  The predicted probabilities and first differences are plotted in Figure 3.

The STATA do file, Figure 3.do, opens the Excel file, Table A3 simqi.xlsx, and plots predicted probabilities and first differences generated from the probit models in Table A3 using CLARIFY.  These first differences are captured in the text files Table A3.log and Table A3 any info.log.


Roadmaps Figure 4 and Tables A5 and A6 in the OA

The STATA do file, Tables A5 and A6.do, opens the analysis dataset, roadmaps_1.dta, recodes variables for treatment and control group assignment, and estimates a probit model of voters� preferences between David Lee and Eric Mar.  The bottom of the file uses the CLARIFY package to generate predicted probabilities and first differences, and compare predicted probabilities and first differences across the control and treatment groups, and levels of political knowledge.  These predicted probabilities and first differences are saved in two text files (Table A5.log and Table A5 any info.log) and one Excel file (Table A5 simqi.xlsx).  The predicted probabilities, first differences and tests of the differences between control and treatment groups are summarized in Table A6 in the OA.  The predicted probabilities and first differences are plotted in Figure 4.

The STATA do file, Figure 4.do, opens the Excel file, Table A5 simqi.xlsx, and plots predicted probabilities and first differences generated from the probit models in Table A5 using CLARIFY.  These first differences are captured in the text files Table A5.log and Table A5 any info.log.


Roadmaps Figure 5

The STATA do file, Figure 5.do, opens the analysis dataset, roadmaps_1.dta, recodes variables for treatment and control group assignment, and conducts difference of means tests of support for the more proximate candidate between the control and each treatment group, and between the voter guide treatment and other treatment groups.  The results of these analyses are saved in one text file (Figure 5.log) and one Excel file (Figure 5 ttests.xlsx).  The bottom of the file opens the Excel file, Figure 5 ttests.xlsx, and plots the proportion of voters supporting the more proximate candidate in the control and each treatment group.


Tables A1 and A2 in the OA

The STATA do file, Tables A1 and A2 Sample.do, opens the file containing voters� responses to the online exit poll from 2012 (roadmaps_1.dta) and tabulates voters� partisanship, sex, race/ethnicity, age, education, household income, homeownership and level of political knowledge.  These variables are summarized in the left-hand columns of Tables A1 and A2 in the OA.

The STATA do file, Tables A1 and A2 Election Day Voters.do, opens the file containing voters� responses to an in-person exit poll in District 1 that we conducted on Election Day 2012 (D1exitpoll_1.dta) and tabulates voters� partisanship, sex, race/ethnicity, age, education, household income, homeownership and level of political knowledge.  These variables are summarized in the middle columns of Tables A1 and A2 in the OA.  The file, D1exitpoll_1_codebook.doc, describes the variables contained in the in-person exit poll file.

The data on the District 1 general population summarized in the right-hand columns of Tables A1 and A2 can be found in the pdf file SupervisorialDistrictProfiles2012_may21.pdf.


Table A7 and Figure A1 in the OA

The STATA do file, Tables A7.do, opens the analysis dataset, roadmaps_1.dta, recodes variables for treatment and control group assignment, and estimates a probit model of Democratic voters� preferences between David Lee and Eric Mar.  The bottom of the file uses the CLARIFY package to generate predicted probabilities and first differences, and compare predicted probabilities and first differences across the control and treatment groups.  These predicted probabilities and first differences are saved in text log files (Table A7.log and Table A7 any info.log) and one Excel file (Table A7 simqi.xlsx).  The predicted probabilities, first differences and tests of the differences between control and treatment groups are summarized in Table A7 in the OA.  The predicted probabilities and first differences are plotted in Figure A1.

The STATA do file, Figure A1.do, opens the Excel file, Table A7 simqi.xlsx, and plots predicted probabilities and first differences generated from the probit models in Table A7 using CLARIFY.  These first differences are captured in the text files Table A7.log and Table A7 any info.log.


Table A8 and Figure A2 in the OA

The STATA do file, Tables A8.do, opens the analysis dataset, roadmaps_1.dta, recodes variables for treatment and control group assignment, and estimates a probit model of voters� preferences between David Lee and Eric Mar, including models with and without demographic control variables.  The bottom of the file uses the CLARIFY package to generate predicted probabilities and first differences, and compare predicted probabilities and first differences across the control and treatment groups.  These predicted probabilities and first differences are saved in four text files (Table A8.log, Table A8 any info.log, Table A8 demo.log, and Table A8 any info demo.log) and one Excel file (Table A8 simqi.xlsx).  The predicted probabilities, first differences and tests of the differences between control and treatment groups are summarized in Table A8 in the OA.  The predicted probabilities and first differences are plotted in Figure A2.

The STATA do file, Figure A2.do, opens the Excel file, Table A8 simqi.xlsx, and plots predicted probabilities and first differences generated from the probit models in Table A8 using CLARIFY.  These first differences are captured in the text files Table A8.log, Table A8 any info.log, Table A8 demo.log, and Table A8 any info demo.log.


Table A9 and Figures A3 and A4 in the OA

The STATA do file, Table A9.do, opens the analysis dataset, roadmaps_1.dta, recodes variables for treatment and control group assignment, and estimates a probit model of voters� preferences between David Lee and Eric Mar.  The bottom of the file uses the CLARIFY package to generate predicted probabilities and first differences, and compare predicted probabilities and first differences across the control and treatment groups, and levels of political knowledge.  These predicted probabilities and first differences are saved in two text files (Table A9.log and Table A9 any info.log) and two Excel files (Table A9 simqi.xlsx and Table A9 any info simqi.xlsx).  The predicted probabilities, first differences and tests of the differences between control and treatment groups are summarized in Table A9 in the OA.  The predicted probabilities and first differences are plotted in Figures A3 and A4.

The STATA do file, Figures A3 and A4.do, opens the Excel files, Table A9 simqi.xlsx and Table A9 any info simqi.xlsx, and plots predicted probabilities and first differences generated from the probit models in Table A9 using CLARIFY.  These first differences are captured in the text files Table A9.log and Table A9 any info.log.


Table A10 in the OA

The STATA do file, Table A10.do, imports the expert survey dataset (expert_1.xlsx), creates and recodes relevant variables as needed, and tabulates experts� ratings of the ideological position of the San Francisco Democratic County Central Committee (DCCC) and San Francisco Republican County Central Committee (RCCC).  The bottom of the file performs a difference-of-means test of the average rating for these two groups.  These data are summarized in Table A10 of the OA.  The file, expert_1_codebook.doc, describes the variables contained in the expert survey file.


Figures A5 and A6

Formatting supervisorial roll calls

The file, sfbos_1_format.do, imports the file sf_bos_2009_2011_divided.xls containing information about every divided roll call taken by the San Francisco Board of Supervisors between January 2009 and January into STATA, creates and recodes relevant variables as needed, and formats these data for analysis (sfbos_1.csv).  The file, sf_bos_2009_2011_divided_codebook.pdf, describes the variables contained in the roll call file.


Estimating supervisorial ideal points

The file, sfbos_1.r, imports the supervisorial roll call dataset sfbos_1.csv into R, formats the data as a roll call object and estimates the ideal points.  The file then saves the resulting dataset (sfbos_1_scores.RData) and exports two comma-separated text files containing the normalized eigenvalues of agreement scores for the San Francisco Board of Supervisors and ideal points of individual supervisors estimated using a one-dimensional spatial model (sfbos_1_eigen.csv, sfbos_1_scores.csv).

The STATA do file, Figure A5.do, imports the text file, sfbos_1_eigen.csv, containing the normalized eigenvalues of agreement scores for the San Francisco Board of Supervisors generated using WNOMINATE, and plots these values.

The STATA do file, Figure A6.do, imports the text file, sfbos_1_scores.csv, containing the ideal points of individual supervisors estimated using a one-dimensional spatial model in WNOMINATE, and plots these values.


Figures A7 and A8 in the OA

The file, sfmay_1.r, imports the mayoral candidate roll call dataset exit_1_cands.csv into R, formats the data as a roll call object and estimates the ideal points.  The file then exports a comma-separated text file containing the ideal points of the candidates running in the 2011 mayoral race estimated using a one-dimensional spatial model (sfmay_1_scores.csv).

The STATA do file, Figure A7.do, imports the text file, sfmay_1_scores.csv, containing the ideal points of individual mayoral candidates estimated using a one-dimensional spatial model in WNOMINATE, and plots these values.

The file, sfsup_1.r, imports the supervisorial candidate roll call dataset exit_3_cands.csv into R, formats the data as a roll call object and estimates the ideal points.  The file then exports a comma-separated text file containing the ideal points of the candidates running in the 2012 supervisorial race estimated using a one-dimensional spatial model (sfsup_1_scores.csv).

The STATA do file, Figure A8.do, imports the text file, sfsup_1_scores.csv, containing the ideal points of individual supervisorial candidates estimated using a one-dimensional spatial model in WNOMINATE, and plots these values.


Table A12 in the OA

The pdf files, SanFrancisco_ACS2011_education.pdf, SanFrancisco_ACS2011_homeownership.pdf, SanFrancisco_ACS2011_income_poverty.pdf, and SanFrancisco_ACS2011_population.pdf, contain the demographic data summarized in Tables A12 under �San Francisco.�  These data are from the 2007-2011 American Community Survey 5-Year Estimates, Tables S1501, DP03, DP04, and DP05.  We generated these data using American FactFinder available at:  https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml.  

The remaining files, Seattle_ACS2011_education.pdf, Seattle _ACS2011_homeownership.pdf, Seattle _ACS2011_income_poverty.pdf, Seattle _ACS2011_population.pdf, Boston_ACS2011_education.pdf, Boston _ACS2011_homeownership.pdf, Boston _ACS2011_income_poverty.pdf, Boston _ACS2011_population.pdf, Portland_ACS2011_education.pdf, Portland _ACS2011_homeownership.pdf, Portland _ACS2011_income_poverty.pdf, Portland _ACS2011_population.pdf, Chicago_ACS2011_education.pdf, Chicago _ACS2011_homeownership.pdf, Chicago _ACS2011_income_poverty.pdf, Chicago _ACS2011_population.pdf, Minneapolis_ACS2011_education.pdf, Minneapolis _ACS2011_homeownership.pdf, Minneapolis _ACS2011_income_poverty.pdf, and Minneapolis _ACS2011_population.pdf, contain the demographic data summarized in Table A12 under �Seattle,� �Boston,� �Portland,� �Chicago,� and �Minneapolis.�  These data are from the 2007-2011 American Community Survey 5-Year Estimates, Tables S1501, DP03, DP04, and DP05.  We generated these data using American FactFinder available at:  https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml.


Table A13 in the OA

The STATA do file, Table A13.do, opens the file containing voters� responses to the exit poll from 2012 (roadmaps_1.dta) and tabulates voters� tabulates voters� sex, race/ethnicity, age, homeownership, partisanship, education, household income, homeownership, political interest, level of political knowledge, ideology and evaluations of local government performance in the control and each treatment group.  The bottom of the file conducts difference of means tests of each variable between each group to assess the effectiveness of survey�s random assignment.  The results of these analyses are summarized in Table A13.


